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PERSONALITY TRAITS AND USER BEHAVIOR A Thesis by CHRISTOPHER RONALD KING Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2011 Major Subject: Computer Science
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  • PERSONALITY TRAITS AND USER BEHAVIOR

    A Thesis

    by

    CHRISTOPHER RONALD KING

    Submitted to the Office of Graduate Studies of

    Texas A&M University

    in partial fulfillment of the requirements for the degree of

    MASTER OF SCIENCE

    December 2011

    Major Subject: Computer Science

  • Personality Traits and User Behavior

    Copyright 2011 Christopher Ronald King

  • PERSONALITY TRAITS AND USER BEHAVIOR

    A Thesis

    by

    CHRISTOPHER RONALD KING

    Submitted to the Office of Graduate Studies of

    Texas A&M University

    in partial fulfillment of the requirements for the degree of

    MASTER OF SCIENCE

    Approved by:

    Co-Chairs of Committee, Frank Shipman William Lively

    Committee Members, Selma Childs

    Jon Jasperson

    Head of Department, Duncan Walker

    December 2011

    Major Subject: Computer Science

  • iii

    ABSTRACT

    Personality Traits and User Behavior. (December 2011)

    Christopher Ronald King, B.S.; B.A., Texas A&M University

    Co-Chairs of Advisory Committee: Dr. Frank Shipman

    Dr. William Lively

    Psychologists and human resources personnel have used personality profiling as

    a predictor of human behavior in various environments for many decades. Knowing the

    personality traits of a particular individual allows management to tailor an environment

    ideally suited for an individual, attempting to maximize a person’s productivity and job

    satisfaction. Measurements of personality are classically achieved through a self-

    reporting survey. This method has a potential inaccuracy due to its lack of objectivity

    and a bias due to cultural influences. This research explores the relationships between

    specific computer user behavior patterns and personality profiles. The results may

    provide a partial map between personality profile traits and computer user behavior.

    In an attempt to discover such correlations, forty-five fraternity and sorority

    students from Texas A&M University were selected to participate in a personality

    survey and three computer based tests. One test measured the subject’s perceptive

    abilities, another measured their decision-making requirements, and a third measured

    their methods employed in organizing a task.

    The results show conclusively that some personality profile traits do influence

    how people visually interpret information presented on a computer screen. Individuals

  • iv

    who exhibit high conscientiousness or agreeableness scores on a personality assessment

    survey take less time to find an icon among a collection during an icon search test.

    However, the results also show a significantly large variability in individuals,

    indicating that many other factors may influence attempts to measure an individual’s

    personality traits. This indicates that the tests presented in this study, even though they

    show that behavior is related to personality traits, cannot be used as diagnostic tools.

    Further research will be required to obtain that goal.

  • v

    DEDICATION

    To my wife, without whose continual encouragement, devotion and love, this

    would not have been possible

  • vi

    TABLE OF CONTENTS

    Page

    ABSTRACT ............................................................................................................. iii

    DEDICATION ......................................................................................................... v

    TABLE OF CONTENTS ......................................................................................... vi

    LIST OF FIGURES .................................................................................................. viii

    LIST OF TABLES .................................................................................................... ix

    1. INTRODUCTION ................................................................................................ 1

    2. EXECUTIVE SUMMARY .................................................................................. 3

    3. STATEMENT OF PROBLEM ............................................................................ 4

    4. HYPOTHESIS - RATIONAL .............................................................................. 6

    5. LITERATURE REVEIW ..................................................................................... 8

    5.1 Initial Work in Personality Profiling ....................................................... 8

    5.2 Current User Modeling Using Personality Profiles ................................ 10

    5.3 Earlier Similar Studies ............................................................................ 10

    6. METHODOLOGY ............................................................................................... 12

    6.1 Overview ................................................................................................ 12

    6.2 Personality Assessment Tool .................................................................. 13

    6.3 Icon Search Task .................................................................................... 14

    6.4 Decision-making Task ............................................................................ 15

    6.5 Organizational Task ................................................................................ 17

    6.6 Testing Environment and Equipment ..................................................... 20

    7. STATITISTICAL ANALYSIS ............................................................................. 22

    7.1 Method ................................................................................................... 22

    7.2 Limitations of the Data ........................................................................... 22

  • vii

    Page

    8. RESULTS ............................................................................................................. 24

    8.1 Results of the Icon Search Task ............................................................. 24

    8.2 Conclusions of the Icon Search Task ..................................................... 28

    8.3 Results of the Decision-making Task ..................................................... 29

    8.4 Conclusions of the Decision-making Task ............................................. 32

    8.5 Results of the Organizational Task ........................................................ 32

    9. CONCLUSIONS ................................................................................................... 33

    REFERENCES ......................................................................................................... 35

    APPENDIX A .......................................................................................................... 37

    APPENDIX B ........................................................................................................... 41

    APPENDIX C ........................................................................................................... 43

    APPENDIX D ........................................................................................................... 44

    APPENDIX E ........................................................................................................... 46

    APPENDIX F ........................................................................................................... 60

    APPENDIX G ........................................................................................................... 61

    VITA . ……............................................................................................................... 63

  • viii

    LIST OF FIGURES

    Page

    Fig. 1. Example Icon Search Screen ......................................................................... 15

    Fig. 2. Decision-Making Interface ............................................................................ 16

    Fig. 3. Project Tracker Main Screen ......................................................................... 18

    Fig. 4. Project Tracker Project Form......................................................................... 18

    Fig. 5. Project Tracker Job Form .............................................................................. 19

    Fig. 6. Search Times for Static vs. Animated Screens ............................................. 25

    Fig. 7. Comparisons of Upper and Lower Conscientiousness Populations .............. 26

    Fig. 8. Comparisons of Upper Quartile and Lower Quartile Conscientiousness

    Populations .................................................................................................... 27

    Fig. 9. Comparisons of Upper and Lower Agreeableness Populations .................... 28

    Fig. 10 Comparisons of Upper Quartile and Lower Quartile Agreeableness

    Populations ................................................................................................. 28

    Fig. 11. Conscientiousness vs. Average Time Reviewing an Answer ..................... 30

    Fig 12. Extraversion vs. Total Time Reviewing Answers to Questions ................... 31

    Fig. 13. Conscientiousness vs. Number of Questions Reviewed ............................. 32

  • ix

    LIST OF TABLES

    Page

    Table 1 Results of ANOVA Analysis ....................................................................... 26

    Table 2 Pearson Correlation Coefficients, N=26 Prob > |r| under HO: Rho=0......... 29

  • 1

    1 INTRODUCTION

    Current practice in obtaining personality measurements is accomplished by using a

    personality assessment survey. Results of these surveys are limited to the qualitative

    realm, relying on an individual’s subjective interpretation of experiences and

    understanding of the definitions of terms. This method poses a problem in that human

    experiences and their interpretation are not objectively reliable. It is known for example

    that some of the personality measurements are subject to the culture in which the person

    has developed from childhood, including the language they speak. Meanings of the

    terms presented on a personality survey are dependent on the cultural definitions, which

    can vary. Any attempt to measure personality without these influences needs to be

    accomplished by measuring the traits via other objective proxy measurements, which are

    unconscious to the individual.

    It is widely accepted that the personality of an individual is developed as a result of

    those traits we inherited combined with a set of life experiences. Individuals use their

    cognitive and physical resources as tools to directly interact with the world. Experience

    via trial and error coupled with feedback forms our habitual methods used to achieve our

    goals. This process eventually shapes people’s personalities.[1] Measuring these two

    inputs, tools and methods, may provide a means of objectively determining an

    individual’s personality traits. Cognitive and perceptual resources such as precognitive

    _____________

    This thesis follows the style of Computer-Aided Design.

  • 2

    awareness are relatively easy to measure with a computer. Life experiences are much

    more difficult to measure. However, methods used to accomplish goals are measurable.

    These methods are manifested in how people approach the world around them and

    interact with it. The past few decades have enabled a world where people are interacting

    more and more via computer interfaces. User profiling should therefore be able to

    measure individual user’s habits along with their cognitive skills and form a reasonably

    accurate assessment of a user’s personality profile. This of course would depend on

    research such as this, which can yield an accurate cross-reference map of specific

    behaviors which are accurate indicators of personality traits.

  • 3

    2 EXECUTIVE SUMMARY

    Research has shown that personality profile traits are highly related to human

    biological traits. These traits, coupled with life experiences, shape an individual’s

    personality. This personality is what guides their behaviors. The intent of this research

    is to measure a limited set of individual user’s cognitive traits and behaviors while using

    a computer interface. This research is then intended to show that these measurements

    have the potential to be used as proxy measurements of user’s personality profiles.

    This study explores three specific methods of measuring behavior as well as

    measuring the personality profile of participants. The data gathered is then analyzed to

    determine if there exists any strong correlations between the measured behaviors and the

    measured personality profile traits.

  • 4

    3 STATEMENT OF PROBLEM

    Personality profile measurement tools lack objective accuracy due to using self-

    descriptive survey tools currently presented in the form of a survey, usually completed

    by an individual being measured. The results are dependent on definitions of terms

    making them dependent on the individual’s vocabulary skills as well as historical

    background, since such definitions vary across cultures, affecting accuracy.

    Additionally, measurements of personality traits through such self-reporting techniques

    pose a set of complex social and psychological problems, which may influence the

    accuracy of the results. People do not always trust the tool or those administering it,

    fearing they might be misjudged, thus may not provide objectively honest information.

    It would be reasonable to assume that individuals might wish to adjust the outcome of

    such a tool, if it were thought that by doing so the individual would gain some

    advantage, posing as a personality other than their own. Some personalities in

    particular are by nature suspicious of being measured in the first place. Others are

    naturally competitive and would want to gain some advantage. Thus the very things that

    are being measured have the potential to modify the results. A method to measure a

    person’s personality that did not rely on self-reporting would be valuable if it were

    passive and objective.

    A personality profile is considered to be the classification of the methods and

    techniques developed and employed by an individual to interact with their environment.

    It is reasonable to assume that recording the actions of an individual while they interact

    with their surroundings would provide an objective measurement. However, capturing

  • 5

    all of the necessary data on a person would prove difficult and thus would not be

    reasonably practical on a scale that could be used in the time frame of a job interview.

    Additionally, unless a mapping was available which reliably converted specific actions

    into reasonably accurate personality profile scores, the recorded data could not be

    interpreted.

    Another method is to attempt to map how a personality is developed by measuring

    the physical and cognitive traits of an individual, which lead to the development of a

    particular personality. The Handbook of Personality states “personality traits are

    exclusively biological in origin.”[2] However, other research indicates that individual

    personalities are modified over time[3,4], indicating life experiences play some role. It is

    therefore reasonable to assume that a person’s personality is developed as a result of

    their biology combined with their life experiences. Experiences in life differ from

    person to person, but for the most part are quite similar for individuals within the same

    society, culture, and demographic group. The remaining influencing factors are physical,

    not limited to genetics passed on from ones parents, but including those derived from

    environmental factors. Some of these physical traits, particularly extreme traits, can be

    very influential in an individual’s personality development. For example, people with

    reduced visual abilities are known on average to have lower Extraversion scores. [4]

    People with poor color discrimination and hearing sensitivity tend to have on average

    increased Neuroticism scores.[5]

  • 6

    4 HYPOTHESIS - RATIONAL

    The basic premise of this research is that there exist specific actions that can be

    measured on an individual basis by a computer interface and provide a proxy

    measurement of an individual’s personality profile.

    If a method of measuring a user’s personality were dependent on unconscious

    actions of a user, then the personality profile of a user could be determined by measuring

    these unconscious actions. To accomplish this, an accurate correlative map must be

    determined between these unconscious actions and personality profile traits. This

    research attempts to determine if such correlations exist by measuring personality

    profiles as well as performance measurements on computer-based tasks. These

    measurements will then be statistically analyzed to determine if correlations exist.

    The justification for this approach is based on the assumption that individual

    personality traits are a result of physical capabilities an individual possesses coupled

    with a set of developed methods to interact with the world. A person with a natural

    strength would more likely develop a personality that uses the strength to maximize their

    success in dealing with their environment. This should be manifested in the subtle

    behavior habits they form.

    Individual differences in users’ behaviors are often subtle and unconscious to

    individuals and can be measured fairly accurately. Using proxy measurements makes

    the user unaware of what is being measured, eliminating the chance that social

    considerations will bias the results.

  • 7

    In the realm of computer interface design, under the category of user modeling

    methods, measurements of user activity can be continually monitored providing

    information to interface designers who aim to build interfaces that maximize the

    effectiveness of the user’s experience.

    In this study, the personality profiles as well as measurements of interactions while

    attempting to accomplish certain tasks was measured on forty-seven volunteers. The

    data was then reviewed to find any statistically relevant correlations between the

    measurements and personality profile traits.

  • 8

    5 LITERATURE REVIEW

    5.1 Initial Work in Personality Profiling

    Personality profiling started in the early part of the twentieth century during which

    time tools such as the well-known Myers-Briggs personality profile were developed.

    Katharine Cook Briggs and her daughter Isabel Briggs developed Myers-Briggs based

    on models proposed by Carl Jung in 1921[6]. Their intent was to develop a profiling

    method during WWII to determine where women would be “most comfortable and

    effective” in wartime jobs. This test, like many of the time, was criticized for not having

    valid convincing data to support the theoretical claims.

    In the later part of the century, there was an attempt to more clearly validate

    personality profiling methods using language taxonomy.[7] In so doing, five major

    categories were determined. These are Openness, Conscientiousness, Extraversion,

    Agreeableness, and Neuroticism. Described here:

    • Openness (O) to Experience/Intellect – High scorers tend to be original,

    creative, curious, complex; Low scorers tend to be conventional, down to

    earth, narrow interests, uncreative.

    • Conscientiousness (C) – High scorers tend to be reliable, well-organized,

    self-disciplined, careful; Low scorers tend to be disorganized,

    undependable, negligent.

    • Extraversion (E) – High scorers tend to be sociable, friendly, fun loving,

    talkative; Low scorers tend to be introverted, reserved, inhibited, quiet.

  • 9

    • Agreeableness (A) – High scorers tend to be good natured, sympathetic,

    forgiving, courteous; Low scorers tend to be critical, rude, harsh, callous.

    • Neuroticism (N) – High scorers tend to be nervous, high-strung, insecure,

    worrying; Low scorers tend to be calm, relaxed, secure, hardy.

    These “Big Five” indicators were determined and tested extensively throughout

    the 1980s to validate them as the predominantly accepted categories of personality types.

    These categories are broad labels of actually sixteen different personality profile traits.

    Most of this work was done to establish that a reliable taxonomy of meanings of words

    used to describe people’s behaviors was consistent. The focus was to establish that the

    traits identified were a complete collection of the terms used in a language, and from this

    the specific categories and the “Big Five” broad categories were determined.

    Thus far all methods used to assess personality profiles depend on the use of

    descriptive assessment tools where subjects would either describe themselves or are

    described by others.

    Throughout the 1990’s, considerable work was done showing that individual

    personality traits can be linked back to biological origins. Studies show that specific

    physical traits have a significant influence on one’s personality. One paper[4] indicates

    that color blindness and acute hearing loss will significantly influence the development

    of ones personality particularly Openness, Extraversion, and Neuroticism. Another

    paper[5] indicates that myopia affects Conscientiousness, Extraversion and Openness.

    These studies show that there are biological links, which can be measured and may

    provide a clue to determining personality traits through the analysis of user behavior.

  • 10

    5.2 Current User Modeling Using Personality Profiles

    Many user modeling approaches have been attempted and researched. Many of

    these model user behavior and use past behaviors to predict future ones. Much of this is

    based on educated guesses on the part of the user model developers regarding that a

    particular behavior means a particular expected need, causing the interface to adjust

    itself accordingly. One such example is the frequently used menu options on the menu

    of Word. Yet, until recently, there has been little work in attempting to develop a user

    model based on personality profiles, considered to be the fundamental predictors of

    human behavior. There is certainly no reliable translation map with correlations between

    specific user behaviors and personality profile traits. Thus there is no way to determine

    that a particular behavior indicates the existence of a particular trait or quantify it.

    5.3 Earlier Similar Studies

    Apart from computer-user profiling studies, there does exist a correlative mapping

    of use-of-language and personality profile traits. One study attempted to extract user

    personality profiles from a linguistic analysis of text written by participants [8]. This

    could prove to be a promising method of determining personality-trait user models, since

    much of what people use computers for is to communicate in written language.

  • 11

    Another attempt closer to the goal of this research was pursued as a side effect

    study during the testing phase of a software system. User personality profiles were

    determined for participants intended to test the software system using the Big Five

    surveys. The data collected during the software test was then used to determine any

    correlations with personality traits [9]. This study showed some correlations.

    Unfortunately, even though the results were promising, the data set was simply not

    statistically conclusive, due to a small number of participants. Other qualitative studies

    where performed attempting to correlate user behavior to personality profiles. [10,11,12]

    None of these studies resulted in a diagnostic tool or attempted to measure specific user

    behavior related to visual or cognitive processes.

    No research has been identified which attempts to find a link between the specific

    user activity metrics measured in this study and personality profiles, specifically

    attempting to determine an alternative diagnostic tool.

  • 12

    6 METHODOLOGY

    6.1 Overview

    The basic hypothesis of this study is that personality profiles are linked to

    measurable physical traits which modify their behaviors. Thus, by measuring a broad

    collection of user skills and behaviors, correlations should be found between these

    measurements and personality profile scores.

    To show this, measurements were taken on participants in three broad categories:

    visual perceptive skills, information analysis in decision-making, and information

    documentation methods used in organizing a defined event.

    Forty-seven individuals participated in a personality profile survey, and performed

    three tasks using computer interfaces. These were an icon search test, a decision making

    task, and an organizational task.

    In the first task, an individual’s perceptive abilities were tested by measuring their

    performance on an icon search test. The task was chosen since vision is clearly a

    primary method used by individuals to gather information about their environment. The

    idea is that a perceptive skills test will be able to measure particular unconscious

    cognitive or visual resources that an individual has that might correlate to different

    personality profile traits. Specifically the expectation is that those with an extreme

    Neuroticism score will show a divergence from an average on a test, which not only

    requires acute attention, but also includes a distractive component.

  • 13

    The second task measures how a participant interacts with an interface to process

    information they need to make a decision. This test specifically measures the quantity of

    information a user considers necessary to make a decision, and the amount of time a

    participant requires to examine information, before making a decision.

    In the final task, participants interact with an interface to organize information in a

    project management system. This test measures the amount of information a user

    perceives is necessary to document a planned event and to some degree the methods

    used in organizing the information.

    Although some results are expected, there are no hypothesized results except that

    correlations will exist, making this entirely a qualitative research study. The goal

    initially is to discover any correlations that are statistically relevant.

    6.2 Personality Assessment Tool

    The Big Five personality assessment tool used is one that is publicly available

    online at: http://www.outofservice.com/bigfive/

    UC Berkeley psychologist Oliver P. John, PhD developed this assessment tool. It

    is similar to other tools available and provides a simple method of obtaining a

    personality profile on the participants in this study.

    Using this tool yields percentile rankings of subjects compared to previous

    individuals who have taken the same test in the past. Using percentile scores allows the

    results to be easily comparable since they are not raw scores but scores that are

    normalized against a large collection of participants. However, there is the possibility

  • 14

    that over time the percentiles would change, as the base would be modified, meaning

    that the percentiles change over time. In the case of the particular set of individuals in

    this study, all participants were tested within a short three-week time frame, against an

    established base of many thousands of participants that had previously used this too.

    Thus, it is assumed that the basis for the percentile scores did not dramatically

    change during the course of the testing of the participants in this study.

    6.3 Icon Search Task

    Each subject was asked to review a series of screens on which an icon would

    appear at the top of the screen and below it, arranged in rows and columns, would appear

    a collection of icons. The participant was instructed to find the icon presented at the top

    of the screen amongst the collection of icons and click on the matching icon as fast as

    possible. The interface would then present a new screen with a different icon to search

    for and a different collection of icons in which it was to be found.

    The entire test consisted of one hundred screens. Each screen would not only

    present a different collection of icons but also odd numbered screens would contain only

    static icons and even numbered screens would include a mixture of animated and static

    icons. The number of icons would increase by one row of twelve more icons every tenth

    screen. Thus the first screen would have a single row of twelve static icons below a

    single target icon presented at the top, the second screen would have twelve icons with

    approximately half of them animated. The eleventh screen would have twenty-four

    static icons to search through and so forth until the last screen would contain one

    hundred and twenty icons. Fig. 1 illustrates a typical screen with eighty four icons.

  • 15

    Fig. 1. Example Icon Search Screen

    The time required for the user to find the target icon and click on it was measured

    for each screen.

    6.4 Decision-making Task

    This experiment was designed based on a classic Turing Test where individuals

    were asked to determine if a set of answers to a set of questions were answered by a

    human or a computer. The purpose of this task was not to replicate the results of a

    Turing Test, or to test how good a particular AI chat program performed during a Turing

    Test. The purpose of this test was to attempt to quantify the amount of information that

    a user felt they needed to consider before they could make a decision.

    In this task the user was presented with a screen of questions that were asked of the

    artificial intelligent (AI) chat program named Alice. The users were not allowed to

  • 16

    immediately see the answers that were given by the chat program, but were instructed

    that if they wished to see the answers they needed only to click on a question, and the

    answer that was given by the respondent to the question would be shown at the top of the

    screen. Buttons were provided at the bottom of the screen allowing the user to make a

    choice. Once the user made a choice the experiment ended.

    The interface for this experiment is shown in Fig. 2.

    Fig. 2. Decision-Making Interface

    Ultimately they were to decide if the answers given to the questions were given by

    an AI program or a person pretending to be an AI program.

    List of questions

    in two columns.

    Answer shown here.

    Buttons to make

    decision.

  • 17

    The analytical measurements explored from the data obtained from this test were

    the following metrics:

    1 Total number of questions examined.

    2 Total time to examine the questions.

    3 The average time spent examining a given answer.

    6.5 Organizational Task

    In this task, participants were instructed in the use of a project

    management/tracking tool named Project Tracker. They were then asked to perform a

    task using the tool. This particular project management tool allows users to create

    Projects and Jobs within those Projects. Both Projects and Jobs within the tool are

    defined as having a Name and a Description. The interface presents a menu of various

    functions, one of which is the function to create Projects. Names of Projects are shown

    in bold with a link to create an associated Job. Names of Jobs are shown below their

    associated Projects, alongside other associated information. These are then presented on

    a screen in a basic two-level outline form, as illustrated in Fig. 3.

  • 18

    Fig. 3. Project Tracker Main Screen

    Creating a Project was accomplished by filling out a form shown in Fig. 4. below.

    Fig. 4. Project Tracker Project Form

    Project title shown here.

    Job titles shown here.

    Link to create a new Project.

    Link to create a new Job.

    Project Title field.

    Project Description field.

    Link to create Project.

  • 19

    Creating a Job was accomplished by filling out the form shown in Fig. 5.

    Fig. 5. Project Tracker Job Form

    Once instructed in the use of the tool, the participants were asked to use the tool to

    plan a dinner party for six people. They were instructed that they had complete freedom

    to plan the party however they liked, but that their plans must include a main course and

    a dessert. Additionally, they were instructed to limit their use of the tool to the creation

    of Jobs and Projects only, and to ignore the other interface elements of the software on

    the main screen, ignore the Notify List field in the Projects form, ignore all elements

    except the Name and the Description fields on the Jobs form.

    Job Name field.

    Job Description field.

    Link to create Job.

  • 20

    During the instruction in the use of the tool, questions related to the use of the tool

    were answered. If participants asked any questions regarding how they should go about

    accomplishing the task, they were instructed that, that was “entirely their decision.”

    Answers to such questions were avoided since any attempt to answer those questions

    could influence the results measured.

    The entire plan created by each participant was saved from which the following

    metrics were derived:

    1 Total number of Jobs.

    2 Total number of Projects.

    3 Total number of Jobs and Projects.

    4 Total number of words used for a Name or Title field.

    5 Total number of words used for a Description field.

    6 Average number of words used for a Name or Title field.

    7 Average number of words used for a Description field.

    8 Total number of words used by a participant

    6.6 Testing Environment and Equipment

    The participants in this study were volunteers predominately from a sorority and a

    fraternity at Texas A&M University.

    In order to avoid bias of results related to the user’s environment, two steps were

    taken. As much care as possible was taken to insure that all participants were tested in

    similar physical environments. It is known in particular that individuals taking the icon

    search task are subject to distraction and therefore distraction was limited as much as

  • 21

    possible, except that which was a deliberate part of the task. This was accomplished by

    asking participants to complete the task in a familiar quiet environment. In this study,

    the tasks were administered in the libraries of the fraternity and sorority houses. Another

    step to avoid bias too into account the age of the participants. Longitudinal studies

    indicate that some personality traits change as a person ages.[4] However, the changes

    are noted over a lifetime and all participants in this study were of college ages ranging

    from nineteen to twenty four, a relatively narrow range.

    All participants were administered the tasks on the same computer hardware. The

    hardware was a Compaq Presario V6000 laptop, using an external mouse. Participants

    were instructed to use the mouse instead of the laptop’s touchpad for the icon search

    task. This insured to some degree that the hardware was not a factor in differences in the

    results.

  • 22

    7 STATISTICAL ANALYSIS

    7.1 Method

    The data for all studies was analyzed using SAS, a statistical analysis tool. The

    analysis of variance (ANOVA) method in SAS software’s proc GLM procedure was

    used to analyze the Icon Search data. This dataset was significantly more complex with

    the existence of animation, and the varying number of icons, considered independent

    tests. The other two datasets were analyzed with a simple correlation also using SAS.

    Any observed correlations were illustrated using averages of measurements of selected

    populations. The populations consisted of those above and below the median values of a

    particular personality trait, and then those in the upper and lower twenty-five percentile

    rankings. These populations were selected exclusively; meaning individuals that were

    equal to any median or quartile boundary value were not considered as a member of

    either population.

    7.2 Limitations of the Data

    The data was derived from a select group of participants who share the common

    trait of being sorority or fraternity members. Thus the results cannot necessarily be

    extended to the general population.

    For the Decision Making Task, and the Project Management Task, the number of

    data points measured will pose a problem with regards to limiting the number of

    participants, which could be grouped into quartiles. Specifically with the Decision

    Making experiment, the scarcity of data was exasperated by the discovery of an

  • 23

    experimental design flaw that made the data obtained on the first twenty-one participants

    unusable. Only twenty-six measurements were obtained from this particular test. In

    addition, the twenty-six participants for whom valid data was obtained were

    overwhelmingly female, since the order of sampling was to sample members of the

    fraternity house first, and then sample members of the sorority house. Thus any

    conclusions drawn from the data may be influenced by a possible gender bias.

  • 24

    8 RESULTS

    8.1 Results of the Icon Search Task

    The Icon Search data presented the largest dataset obtained during the study. Each

    participant was asked to find one target icons on each of one hundred different screens.

    With forty five participants this gave 4500 different raw measurements. Thus to reduce

    the total and to average out considerable variability, time each participant used to locate

    an icon was averaged across screens that varied by the number of icons and whether or

    not there was animation defined. This resulted in twenty measurements for each

    individual or 900 samples total.

    The data indicated that the presence of animated icons did influence the time that

    the users took to find an icon, generally showing that the existence of animated icons

    hampered participants from finding the target icon when the number of icons was low

    (=

    72). This agreed with other studies on icon searches where a group difference aided in

    finding icons.[13] However, there was no evidence that animation coupled with a

    particular personality profile trait aided or hindered disproportionately an individual’s

    ability to find an icon. The results of the times for both animated and static screens are

    shown in Fig. 6.

  • 25

    Static vs Animated

    0

    2

    4

    6

    8

    10

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Se

    arc

    h T

    ime

    (s

    ec

    on

    ds

    )

    Static

    Animated

    Fig. 6. Search Times for Static vs. Animated Screens

    The ANOVA analysis modeled Time against each of the personality profiles as

    well as the influence of the presence of animated icons. The source code and detailed

    results are presented in Appendix A. Table 1 shows the final results of the ANOVA

    analysis using a simple model of Time as a dependent variable to the various personality

    profile percentiles. The p-value of 0.0103 from the analysis indicates that

    Conscientiousness (C) has a statistically significant influence on the icon search time,

    and the negative value on the Estimate for C, shows that a higher C value will yield a

    lower search time. In addition, a slight trend can be shown where a p value of 0.0944 for

    Agreeableness (A) indicates a slight statistically significant influence on search times,

    indicating a higher A score will yield a lower search time.

  • 26

    Table 1 Results of ANOVA Analysis

    Parameter Estimate Standard Error t value Pr > |t|

    O 0.001406981 0.00284860 0.49 0.6215

    C -0.008952927 0.00348304 -2.57 0.0103

    E 0.003539169 0.00312065 1.13 0.2571

    A -0.005689763 0.00339815 -1.67 0.0944

    N -0.003109178 0.00436861 -0.71 0.4768

    Animation 0 7.980774532 B 0.49484100 16.13

  • 27

    This shows that populations of individuals with Conscientiousness scores above

    the median were able to find icons on average faster than those below the median as

    illustrated in Fig. 7.

    Fig. 8. shows a comparison of populations comprising the upper and lower twenty-

    five percentiles. This comparison illustrates the same results and the separation is

    magnified.

    y = 0.0501x + 1.1353

    R2 = 0.9735

    y = 0.0566x + 1.1519

    R2 = 0.9715

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Searc

    h T

    imes (

    seco

    nd

    s)

    C > 25%

    C < 25%

    Linear (C > 25%)

    Linear (C < 25%)

    Fig. 8. Comparisons of Upper Quartile and Lower Quartile Conscientiousness

    Populations

    A similar correlation was determined for populations of individuals with

    Agreeableness scores above and below the median value. This is illustrated in Fig. 9

    below.

  • 28

    y = 0.0519x + 1.3563

    R2 = 0.9743

    y = 0.049x + 1.0474

    R2 = 0.9811

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Searc

    h T

    ime (

    seco

    nd

    s)

    A > 50%

    A < 50%

    Linear (A < 50%)

    Linear (A > 50%)

    Fig. 9. Comparisons of Upper and Lower Agreeableness Populations

    Again, this effect is magnified when the upper and lower twenty-five percentiles

    populations are compared, as shown in Fig. 10.

    y = 0.0481x + 1.1292

    R2 = 0.9348

    y = 0.0535x + 1.4451

    R2 = 0.9577

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Se

    arc

    h T

    ime

    s (

    se

    co

    nd

    s)

    A > 25%

    A < 25%

    Linear (A > 25%)

    Linear (A < 25%)

    Fig. 10. Comparisons of Upper Quartile and Lower Quartile Agreeableness Populations

    8.2 Conclusions of the Icon Search Task

    • On average, high Conscientiousness and Agreeableness scores tend to

    reduce the time required for a given population to find an icon on a screen.

  • 29

    • There was no indication in this study that Openness, Extraversion or

    Neuroticism have any influence on the time required for an individual to

    find an icon on a screen.

    • The use of animated icons increased the overall time required to find an

    icon, but it did not correlate with any personality profile trait as a cofactor.

    8.3 Results of the Decision-making Task

    The data for this test was considerably less complex in that the personality ranks

    could be directly compared to the measured results without averaging. To compare the

    results, Pearson Correlation Coefficients were calculated to determine if there were any

    direct correlations in the data and are shown in the Table 2. Linear fits were then used to

    illustrate these results.

    Table 2

    Pearson Correlation Coefficients, N=26, Prob > |r| under HO: Rho=0

    O C E A N

    NumQ -0.03801

    0.8537

    0.29380

    0.1452

    -0.07465

    0.7170

    -0.24523

    0.2274

    0.09077

    .06592

    Total Time 0.10936

    0.5949

    -0.30821

    0.1256

    -0.48074

    0.0129

    0.13651

    0.5061

    -0.09418

    0.6472

    AverageTime 0.20539

    0.3141

    -0.47322

    0.0146

    -0.23014

    0.2580

    0.11028

    0.5917

    0.11135

    0.5881

    The correlation coefficient P < 0.05 indicates there should exist a correlation

    between Conscientiousness and Average Time spent reviewing a question, and also

    between Extraversion and the Total Time spent participating in the test.

  • 30

    Fig. 11 illustrates these findings providing a scatter plot of the two parameters of

    Average Time vs. Conscientiousness and show that there is a linear fit of the data with

    an R2 value of 0.2239.

    Average Time per Question Reviewed

    y = -0.0138x + 1.5253

    R2 = 0.2239

    0

    0.5

    1

    1.5

    2

    2.5

    3

    0 20 40 60 80 100 120

    Contientiousness

    Se

    co

    nd

    s

    Fig. 11. Conscientiousness vs. Average Time Reviewing an Answer.

    A similar relationship is illustrated in Fig. 12 as a scatter plot of the data showing

    Total Time reviewing questions against Extraversion scores. This yields a linear

    relationship with an R2 value of 0.2311.

    Conscientiousness

  • 31

    Total Time Reviewing Questions

    y = -0.036x + 7.2933

    R2 = 0.2311

    0

    2

    4

    6

    8

    10

    12

    0 20 40 60 80 100 120

    Extraversion

    Seco

    nd

    s

    Fig. 12. Extraversion vs. Total Time Reviewing Answers to Questions

    Furthermore, it would not make sense that the Average Time spent reviewing

    questions would be smaller, without one of the two parameters that are used to calculate

    the Average Time correlating to Conscientiousness. Fig. 13 shows that a relationship

    does seem to exist. However, the fit of the line is influenced greatly by the two data

    points with low C values and high number of questions reviewed. With these removed,

    the linear fit is much improved.

  • 32

    C vs numQ

    y = 0.3565x - 11.495

    R2 = 0.3918

    y = 0.163x + 5.4121

    R2 = 0.0863

    0

    5

    10

    15

    20

    25

    30

    35

    40

    0 20 40 60 80 100 120

    Contentiousness

    Nu

    mb

    er

    of

    Qu

    esti

    on

    s

    Revie

    wed

    Fig. 13. Conscientiousness vs. Number of Questions Reviewed

    8.4 Conclusions of the Decision-making Task

    • The data shows a correlation between Conscientiousness and both the

    Number of Questions that a participant reviewed, as well as a correlation

    with the Average Time spent reviewing answers to questions.

    • In addition, the data shows a good correlation between the Total Times

    spent reviewing the data and participants Extraversion scores.

    8.5 Results of the Organizational Task

    In reviewing the data gathered from the Organizational Tasks, the same method

    was used as that used for the Decision-Making Task. Pearson Correlation Coefficients

    were calculated on the dataset and these are shown in Appendix C. Unfortunately there

    appeared to be no correlations, even weak ones, between the parameters examined and

    any of the personality profile characteristics.

    Conscientiousness

  • 33

    9 CONCLUSIONS

    The results of the three tests indicate that personality profiles are related to the way

    in which people interact with computers. However, results vary widely from individual

    to individual. This study would seem to indicate that none of the tests presented here

    could be used as a proxy measure of a user’s personality profile.

    The data indicates that Conscientiousness was related to how quickly an individual

    could find an icon. It was also shown that a person with a high Conscientiousness score

    spent less time reading the answer to a question. These results, combined, hint at a

    relationship between Conscientiousness and visual perceptive skills. Further research

    may yield a specific test that measures visual perceptive skills more closely and

    determine if such skills correlate strongly with Conscientiousness.

    Yet the cause of these results is not entirely clear. The lower search times, in both

    the time a participant spent finding an icon, as well as the time spent reading the answers

    to questions, could indicate that an individual with high Conscientiousness had more

    acute visual abilities, allowing them to not only find the icon but to read faster.

    However, the same result could be achieved if they simply made decisions faster.

    Tolerance to fatigue could have played a role in the differences in search times since the

    icon search task had the participants review a very large number of screens.

    Although none of results rule out the potential that these tests will yield some idea

    of an individual’s personality, the variability indicates that personality profiles influence

    individual user’s behavior less specifically than hypothesized. Future studies can be

  • 34

    designed to test for these influences to determine more clearly what physical or cognitive

    abilities can be measured to provide a proxy measurement for personality profiles.

    It should be noted that personality profiles tests are a qualitative descriptive

    measurement. Their lack of accuracy is the very problem attempting to be addressed in

    this study. The statistical variability shown here may be a result of the inaccuracy of the

    current methods used to measure personality profiles, not in the inaccuracies of the

    quantitative measurements of these tests. In addition to other tests mentioned above,

    further research would need to be conducted with much larger trials to determine if the

    suggested correlations in this work actually exist to a high enough degree of accuracy to

    be used as proxy tools.

  • 35

    REFERENCES

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    [3] Robins RW, Fraley RC, Roberts BW, Trzesniewski1 KH. A longitudinal study of

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    [4] Coren S, Harland RE. Personality correlates of variations in visual and auditory

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    [6] Jung CG. Psychological Types (Collected Works of C.G. Jung), vol.6. Princeton,

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    systems: personality and people’s attitudes. In: Proceedings of the 10th

    international conference on intelligent user interfaces. 2005. p. 223-230.

  • 36

    [12] Lisetti CL, Brown S, Alvarez K, Marpaung A. A social informatics approach to

    human-robot interaction with an office service robot. In: IEEE transactions on

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    [13] Niemeia M, Saarinen J. Visual search for grouped versus ungrouped icons in a

    computer interface. Human Factors 2000;42(4):630-635.

  • 37

    APPENDIX A

    Icon Search Results

    SAS program

    PROC GLM;

    CLASS Animation Icons;

    MODEL Time = O C E A N Animation |Icons / noint solution;

    RUN;

    Table A.1

    Results of SAS Program.

    The SAS System The GLM Procedure

    Number of Observations Read 900

    Number of Observations Used 900

    Dependent Variable:

    Time

    Sum of

    Source DF Squares Mean Square F Value Pr > F

    Model 25 22661.59188 906.46368 179.94 F

    O 1 11967.79346 11967.79346 2375.68

  • 38

    O 1 1.228968 1.228968 0.24 0.6215

    C 1 33.284216 33.284216 6.61 0.0103

    E 1 6.479459 6.479459 1.29 0.2571

    A 1 14.123049 14.123049 2.8 0.0944

    N 1 2.551703 2.551703 0.51 0.4768

    Animation 1 2.192296 2.192296 0.44 0.5096

    Icons 9 3028.842969 336.538108 66.8

  • 39

    y = 0.0543x + 1.1047

    R2 = 0.9785

    y = 0.0497x + 1.1315

    R2 = 0.9662

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Searc

    h T

    ime (

    seco

    nd

    s)

    C > 50%

    C < 50%

    Linear (C < 50%)

    Linear (C > 50%)

    Fig. A.1 Conscientiousness - Comparison of Upper and Lower Groups

    Fig. A.2 shows a comparison of populations comprising the upper and lower

    twenty-five percentiles. This comparison manifests the same results and, as would be

    expected, the trend is magnified.

    y = 0.0501x + 1.1353

    R2 = 0.9735

    y = 0.0566x + 1.1519

    R2 = 0.9715

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Searc

    h T

    imes (

    seco

    nd

    s)

    C > 25%

    C < 25%

    Linear (C > 25%)

    Linear (C < 25%)

    Fig. A.2 Conscientiousness - Comparison of Upper and Lower Quartiles

  • 40

    y = 0.0519x + 1.3563

    R2 = 0.9743

    y = 0.049x + 1.0474

    R2 = 0.9811

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Searc

    h T

    ime (

    seco

    nd

    s)

    A > 50%

    A < 50%

    Linear (A < 50%)

    Linear (A > 50%)

    Fig. A.3 Agreeableness - Comparison of Upper and Lower Quartiles

    Again, this effect is magnified when the upper and lower twenty-five percentiles

    are compared, as shown in Fig. A.4 below.

    y = 0.0481x + 1.1292

    R2 = 0.9348

    y = 0.0535x + 1.4451

    R2 = 0.9577

    1

    2

    3

    4

    5

    6

    7

    8

    0 12 24 36 48 60 72 84 96 108 120 132

    Num Icons

    Searc

    h T

    imes (

    seco

    nd

    s)

    A > 25%

    A < 25%

    Linear (A > 25%)

    Linear (A < 25%)

    Fig. A.4 Agreeableness - Comparison of Upper and Lower Quartiles

  • 41

    APPENDIX B

    Statistical Analysis of Decision Making Task

    Table B.1

    Pearson Correlation Coefficients, N=26, Prob > |r| under HO: Rho=0

    O C E A N

    NumQ -0.03801

    0.8537

    0.29380

    0.1452

    -0.07465

    0.7170

    -0.24523

    0.2274

    0.09077

    .06592

    Total

    Time

    0.10936

    0.5949

    -0.30821

    0.1256

    -0.48074

    0.0129

    0.13651

    0.5061

    -0.09418

    0.6472

    Average

    Time

    0.20539

    0.3141

    -0.47322

    0.0146

    -0.23014

    0.2580

    0.11028

    0.5917

    0.11135

    0.5881

    Average Time per Question Review ed

    y = -0.0138x + 1.5253

    R2 = 0.2239

    0

    0.5

    1

    1.5

    2

    2.5

    3

    0 20 40 60 80 100 120

    Contientiousness

    Seco

    nd

    s

    Fig. B.1 Conscientiousness vs. Average Time Reviewing an Answer

    Total Time Review ing Questions

    y = -0.036x + 7.2933

    R2 = 0.2311

    0

    2

    4

    6

    8

    10

    12

    0 20 40 60 80 100 120

    Extraversion

    Seco

    nd

    s

    Fig. B.2 Extraversion vs. Total Time Reviewing Questions

    Conscientiousness

  • 42

    C vs numQ

    y = 0.3565x - 11.495

    R2 = 0.3918

    y = 0.163x + 5.4121

    R2 = 0.0863

    0

    10

    20

    30

    40

    0 20 40 60 80 100 120

    Contentiousness

    Nu

    mb

    er

    of

    Qu

    esti

    on

    s

    Revie

    wed

    Fig. B.3 Conscientiousness vs. Number of Questions Reviewed

    Conscientiousness

  • 43

    APPENDIX C

    Analysis of Organizational Task

    Table C.1

    Pearson Correlation Coefficients, N=39, Prob > |r| under HO:Rho=0

    O C E A N

    numjobs 0.0390 -0.1185 -0.4754 0.0086 0.0906

    0.8139 0.4724 0.0022 0.9585 0.5832

    numprojects 0.1655 -0.1247 -0.1748 0.0783 0.0987

    0.3140 0.4495 0.2873 0.6358 0.5500

    NumNobsandProjects 0.1564 -0.1319 -0.2335 0.0724 0.1039

    0.3417 0.4236 0.1525 0.6613 0.5292

    TitleWords 0.0331 -0.0538 -0.0731 0.0764 0.1678

    0.8414 0.7448 0.6582 0.6439 0.3071

    DescriptiveWords 0.2559 -0.2164 -0.0267 -0.0637 0.0570

    0.1158 0.1857 0.8718 0.6999 0.7305

    AverageTitle -0.2673 -0.0376 0.1132 -0.1976 0.0122

    0.1000 0.8203 0.4928 0.2280 0.9414

    AverageDescription 0.0634 -0.1889 0.1841 -0.2405 -0.0832

    0.7014 0.2496 0.2619 0.1403 0.6146

    Total_Words 0.2552 -0.2184 -0.0338 -0.0548 0.0733

    0.1169 0.1816 0.8383 0.7402 0.6576

    No correlations were determined. There would possibly be a correlation between

    Extraversion and the number of Jobs, but when looking at the data there where only five

    individuals who used more than one job. This seemed to be more of a misunderstanding

    of the outlining capability of the tool and not statistically relevant.

  • 44

    APPENDIX D

    Raw Personality Profile Measurements.

    Idcode's O C E A N

    16183E 53 69 42 79 43

    1651CA 7 98 59 97 1

    1a39de 1 79 27 27 76

    1C5882 65 95 70 83 22

    1E1E75 70 74 70 97 37

    2018DB 12 74 70 74 27

    292EDE 16 92 37 96 27

    2d0dbc 76 97 42 96 27

    2da18e 20 74 70 63 18

    2EBCCC 65 83 79 87 37

    32AF92 7 89 83 22 18

    339A4C-B 2 94 64 94 5

    33E973 53 46 53 93 43

    36DADB 59 6 64 83 22

    36DEAB 20 64 79 83 22

    3824D3 4 95 93 79 14

    3B45E9 35 46 18 44 22

    3f0272 70 10 59 83 9

    434EFD 59 89 27 83 66

    48b200 2 64 93 83 27

    49D9A4 20 94 42 93 43

    4a64E0 90 79 48 93 18

    4C51ED 4 64 86 94 5

    50FA05 12 79 83 87 4

    5abo3f 41 69 70 74 18

    5BFC20 84 74 86 93 43

    5DD4332 12 30 93 6 14

    5E7C0B 35 86 97 38 43

    5EE7CF 30 74 42 74 9

    5FF4BE 35 97 95 79 9

    619A45 30 86 83 69 55

    64C7C1 88 79 74 90 32

    6A7527 84 52 18 74 32

    6C8489 1 35 79 74 27

    6da768 53 52 79 22 66

    77E07B 2 30 31 22 3

    7aaa97 2 52 83 63 66

  • 45

    7Ac26E 10 79 86 96 18

    7bb4aa 16 58 91 83 32

    7d2c6a 76 89 96 87 43

    7E2A17 76 74 97 94 27

    80758e 12 41 12 93 27

    81086B 70 35 22 38 55

    83f325 47 83 53 4 80

    F7B78 41 52 37 87 5

    Table D.1 Personality Profile Measurements Raw Data

  • 46

    APPENDIX E

    Raw Icon Search Times

    Idcodes Screen1 Screen2 Screen3 Screen4 Screen5 Screen6 Screen7 Screen8

    16183E 2.28378 2.59577 1.53253 1.62617 1.6523 1.80999 1.71251 1.37819

    1651CA 2.14609 1.63639 1.54021 1.37647 1.54868 1.33436 1.94062 1.2805

    1a39de 2.65333 1.73938 1.78309 1.31592 1.75546 1.30784 2.52561 1.24367

    1C5882 2.11441 1.46916 1.65455 1.41299 1.65098 1.36109 1.77695 1.16515

    1E1E75 3.58092 1.96052 1.93764 1.6367 1.75766 1.84497 1.74901 1.43075

    2018DB 2.46431 1.70064 1.99363 1.81886 1.77467 1.56492 2.22096 1.62501

    292EDE 2.80431 1.78776 1.81387 1.85947 2.20805 1.44602 1.36401 1.41174

    2d0dbc 2.14603 1.56468 1.60414 1.3482 1.77422 1.62876 1.55811 1.31044

    2DA18E 6.94931 2.28702 2.1248 1.74066 2.23341 1.47542 1.81911 1.7114

    2EBCCC 2.42711 1.85332 1.53947 1.90363 1.82752 1.46938 1.85529 1.33161

    32AF92 2.44432 1.94076 1.57644 2.39669 2.62927 1.59904 1.9908 1.76115

    339A4C-B 1.96373 1.40378 1.65174 1.42588 1.90803 1.76622 1.11597 0.924119

    33E973 2.98631 1.64343 1.75592 1.3396 1.97588 1.37956 1.39975 1.12418

    36DADB 3.69039 1.8243 1.91707 2.03681 2.16466 1.62905 2.07081 1.47285

    36DEAB 2.59336 1.7077 2.1901 1.38341 2.54732 1.8134 1.73925 1.54504

    3824D3 3.0694 2.36994 1.85338 2.08315 2.33183 1.64952 2.41606 1.4439

    3B45E9 2.71074 2.48628 3.09327 3.51235 2.80261 1.97871 1.38238 1.41047

    3F0272 2.61976 2.43206 1.87307 1.88386 2.73139 1.72179 3.0336 1.3878

    434EFD 2.81433 1.7958 1.62017 1.67988 1.68813 1.60595 1.62832 1.25425

    48B200 2.81187 1.81704 1.73333 1.27533 1.74757 1.59752 2.22134 1.33114

    49D9A4 2.87427 1.84491 1.70117 1.76867 1.99098 1.34477 2.00497 1.14917

    4A64E0 2.3989 1.54262 2.21072 1.59318 2.56323 1.81124 1.48083 1.24907

    4C51ED 2.37788 1.70926 1.60675 1.7736 1.91577 1.56344 1.87548 1.26771

    50FA05 5.63265 2.08261 1.53878 1.23869 1.57478 1.76498 1.58882 1.33473

    5abo3f 3.00267 1.54606 1.29969 1.70393 1.89996 1.47203 1.23381 1.31637

    5BFC20 2.32874 1.43222 1.61677 1.93323 1.94739 1.54107 1.42478 1.38113

    5DD4332 1.72164 1.72974 1.63176 1.80999 1.57565 1.41202 1.18529 1.37963

    5E7C0B 2.22715 1.35 1.37512 1.30711 1.68547 1.25937 1.7678 1.04538

    5EE7CF 3.65735 2.09488 1.91581 1.81986 2.44004 1.72803 2.11816 1.6619

    5FF4BE 3.90514 2.74635 1.92447 2.10048 2.22489 1.58072 1.60888 1.68653

    619A45 2.44149 1.8341 1.95571 1.78812 2.07792 1.73453 1.42871 1.27007

    64C7C1 2.08317 1.61082 1.58467 1.58128 1.76891 1.60731 1.23713 1.54967

    6a7527 6.19187 1.80977 1.656 1.4917 1.84222 1.51397 1.10885 1.0865

    6C8489 2.27718 1.70157 1.66558 1.87594 1.92317 1.53185 2.05765 1.43795

    6DA768 2.00003 1.37653 1.41244 1.96308 1.93045 1.53682 2.58834 1.39106

    77E07B 2.92524 1.75637 1.77151 1.47347 1.9396 1.65547 1.2839 1.29972

    7aaa97 2.16423 1.89269 1.71447 1.16469 2.24451 1.4206 1.79264 1.45858

    7Ac26E 5.4868 2.98959 4.32669 3.98114 3.23318 2.28338 1.5792 1.41594

    7bb4aa 2.97409 1.74796 1.73437 1.51668 2.30027 1.9765 1.62057 1.35245

    7d2c6a 2.53236 1.98698 2.14873 1.69502 2.21926 1.80702 1.34109 1.24951

    7E2A17 4.56306 1.5067 1.50107 1.74524 1.8512 1.68114 1.4014 1.26535

  • 47

    80758E 1.86835 1.43085 1.32835 1.23853 1.67883 1.54271 1.41064 1.0088

    81086B 4.19211 1.95403 1.89292 1.59403 1.74058 1.5286 1.59046 1.52009

    83F325 3.79487 1.97282 1.56321 1.68295 2.29134 1.83305 2.58737 1.55733

    F7B78 1.67043 1.22451 1.27664 1.14869 1.50248 1.31478 0.972601 1.07438

    Idcodes Screen9 Screen10 Screen11 Screen12 Screen13 Screen14 Screen15 Screen16

    16183E 1.6569 2.37032 2.69701 3.35027 2.15084 4.72129 1.80154 2.41761

    1651CA 1.5771 1.49281 1.78718 3.29334 2.08324 1.53136 1.65707 1.9416

    1a39de 1.78768 2.52626 1.75541 3.11712 3.84425 1.42875 1.50223 3.08314

    1C5882 1.69088 1.64149 1.76129 4.11399 3.06769 3.63813 1.41419 2.12401

    1E1E75 1.71323 2.03514 2.14719 4.80607 2.89976 1.99187 1.75454 1.97196

    2018DB 1.83516 1.755 1.67694 3.81416 2.19554 3.51768 1.54768 2.14209

    292EDE 1.76997 2.29616 2.02857 3.61502 2.69875 4.50333 2.41082 2.13699

    2d0dbc 1.44948 1.54245 1.6666 1.88522 1.571 1.63297 1.79492 2.1739

    2DA18E 1.72939 1.86352 1.74736 6.40622 2.31599 2.99627 1.70013 2.27245

    2EBCCC 1.67374 1.6918 1.57009 2.84833 2.04612 2.50049 1.86422 1.91684

    32AF92 1.72887 2.04725 2.25309 5.08373 2.98789 2.40596 1.684 3.12413

    339A4C-B 1.54018 1.95291 1.59408 4.15713 2.5247 2.49307 1.89108 3.48128

    33E973 1.7355 1.98788 1.414 3.19594 2.22034 1.27292 1.69059 1.40905

    36DADB 1.41885 2.03186 2.05717 3.76545 2.12353 4.50873 1.85348 3.64402

    36DEAB 1.79338 2.11623 2.32387 3.87627 2.99406 3.64651 2.28472 3.04505

    3824D3 1.68393 2.60384 1.82776 2.59816 5.61413 2.4521 1.60239 1.73843

    3B45E9 2.00107 1.9286 1.84694 2.66959 5.26377 2.60381 2.28342 1.93946

    3F0272 1.80593 1.60391 1.7638 2.51681 3.14007 1.68064 2.08828 11.4549

    434EFD 1.53202 2.17022 1.94401 4.84472 2.16463 1.6689 1.93477 2.43708

    48B200 1.55914 1.82374 1.86776 2.17405 6.86812 4.32664 1.91676 1.55655

    49D9A4 1.63084 1.92105 1.94511 2.18854 2.86216 4.27392 1.4516 1.81803

    4A64E0 1.89529 2.46714 1.78907 2.57544 1.9397 2.43169 2.02566 1.4916

    4C51ED 1.60681 2.45216 1.36945 4.60114 4.00848 6.32854 2.14829 2.92674

    50FA05 1.82116 2.71428 1.65109 7.97755 1.72636 1.24347 1.01171 2.16177

    5abo3f 1.35592 2.04423 1.33202 4.13899 3.95878 1.7349 2.06947 1.5828

    5BFC20 1.58504 1.7871 1.39702 3.80601 3.37193 2.14598 1.46822 1.71795

    5DD4332 1.59165 1.90587 1.65185 3.77931 2.26236 2.67485 2.10046 2.06524

    5E7C0B 1.76362 2.22022 1.6876 4.42219 1.39417 1.86064 1.84814 1.55081

    5EE7CF 1.96991 2.70414 1.99598 5.01286 3.97087 5.81947 2.03698 2.52535

    5FF4BE 1.98899 2.13502 1.91506 4.17946 2.03955 1.7357 1.81969 1.83176

    619A45 1.95273 2.01036 2.23252 4.25123 4.54511 3.88328 3.82729 2.34924

    64C7C1 1.55955 2.17523 1.53947 4.16023 1.90401 4.72417 1.39552 1.35583

    6a7527 1.54022 1.68851 1.16619 2.29286 2.45871 1.47075 1.5766 1.93362

    6C8489 1.79764 1.68583 1.70033 2.62441 2.03653 1.72472 2.05222 1.85869

    6DA768 1.64853 1.67901 1.83076 6.24962 5.92794 2.42488 1.40951 2.06397

    77E07B 1.93612 1.64699 1.84259 4.12268 4.25902 2.67883 2.31855 1.89483

    7aaa97 1.80682 1.66864 1.81499 2.90111 2.77779 3.9898 2.57548 1.41168

    7Ac26E 1.54734 2.01347 1.86779 4.39086 2.45612 2.55682 4.02814 3.7987

    7bb4aa 1.4985 2.01441 1.6769 3.26948 4.88733 3.21339 1.36356 2.15736

  • 48

    7d2c6a 1.5987 1.98337 1.83577 3.00353 2.39564 4.57232 1.89785 2.40623

    7E2A17 1.6751 1.77126 1.68173 5.02638 2.23696 4.09225 1.52271 2.40254

    80758E 1.7188 1.54286 1.79494 2.22131 2.00929 1.41681 1.36364 1.15555

    81086B 1.50871 2.24215 2.19444 2.39666 4.94915 2.21804 1.61929 2.51963

    83F325 1.79745 2.81318 2.01959 5.11994 2.75209 4.02669 1.9879 2.17423

    F7B78 1.42076 2.34464 1.34889 1.55102 1.4737 1.61126 1.43401 1.44305

    Idcodes Screen17 Screen18 Screen19 Screen20 Screen21 Screen22 Screen23 Screen24

    16183E 1.98159 1.73758 2.32543 2.05997 1.73565 2.35646 2.06024 3.44865

    1651CA 2.28125 1.506 1.64339 2.38775 1.9895 7.72063 2.5525 2.13105

    1a39de 2.93863 2.76671 1.93302 2.14696 2.28483 1.71746 2.42176 2.54543

    1C5882 2.23598 1.20394 5.82049 1.83226 1.81037 9.83344 1.93095 2.53724

    1E1E75 1.90585 1.70047 1.74472 2.24422 2.43645 2.24514 2.06721 1.85259

    2018DB 2.36195 1.85113 1.94445 2.06042 2.39208 1.93881 2.57477 2.12557

    292EDE 1.95708 4.89327 1.83588 1.89198 1.90504 3.3317 2.89781 5.56635

    2d0dbc 1.53887 1.08078 2.03225 1.74545 2.27546 2.05157 1.87387 3.3802

    2DA18E 5.33848 1.63833 2.28653 1.96499 1.78038 7.74533 2.66028 2.67159

    2EBCCC 1.78844 1.49475 1.98469 1.90468 1.78834 3.59963 2.22303 1.85935

    32AF92 6.81212 1.7224 2.72343 2.15457 3.18034 6.73725 2.86114 2.51306

    339A4C-B 1.61859 1.9733 1.70691 1.86961 2.68753 2.39844 1.97385 1.94158

    33E973 2.20671 1.60536 2.26759 1.83714 1.77286 4.6441 2.27557 1.75596

    36DADB 3.50866 6.95068 1.89617 1.86846 1.99809 2.14314 2.77894 5.75119

    36DEAB 3.69089 2.8486 2.79869 2.3273 2.54083 2.50555 1.81982 2.52773

    3824D3 2.12645 1.48487 2.27696 1.42896 1.65525 1.97941 1.67956 10.6241

    3B45E9 4.56785 1.75773 2.26196 2.7544 2.92405 2.50061 2.35283 3.26713

    3F0272 5.00497 1.58099 2.34513 2.14703 1.90127 6.88196 2.47406 1.78159

    434EFD 2.10105 1.56211 1.57761 2.26195 2.03333 2.62779 1.91836 4.19809

    48B200 2.2125 3.69248 2.21659 1.66157 1.53707 8.94995 1.94437 3.01256

    49D9A4 2.14981 1.29624 1.80389 2.3883 1.75015 1.92071 2.36868 2.60722

    4A64E0 2.44302 2.32201 2.58645 2.17201 1.6742 2.52473 2.01261 3.56375

    4C51ED 2.32414 1.61322 2.25966 1.87148 2.35413 1.78331 2.15137 1.91442

    50FA05 1.79421 3.37225 2.75401 1.38829 2.01419 3.82938 2.14248 9.51564

    5abo3f 1.62722 1.58102 3.42544 1.54743 2.05359 6.28391 2.82026 2.12858

    5BFC20 4.43027 2.19319 3.33833 1.88712 1.81277 9.82718 1.95729 1.76512

    5DD4332 1.40279 1.34672 2.44512 1.81906 1.75922 3.85562 2.26753 4.93401

    5E7C0B 1.99267 2.79457 2.24475 1.44492 1.64489 2.42138 1.59334 2.43559

    5EE7CF 6.52322 4.91665 3.94812 2.09383 3.24416 21.8667 2.3188 2.02301

    5FF4BE 1.70583 2.02821 2.24614 1.93234 1.87404 3.50893 2.03833 2.26309

    619A45 2.17625 1.43512 1.80399 2.29959 2.13627 2.3243 3.90816 2.54236

    64C7C1 2.66045 1.14411 2.39635 2.04426 1.60411 2.3244 1.8205 2.0892

    6a7527 1.79472 1.73312 1.37115 1.83351 2.06703 1.78183 1.94596 1.83576

    6C8489 1.7665 1.85285 1.89076 2.12885 2.18294 1.88347 2.08334 1.94748

    6DA768 2.25158 2.40226 9.61088 2.16862 1.49422 3.42084 2.32892 2.23911

    77E07B 2.14156 1.63316 4.11825 2.01096 2.49716 8.8379 2.46977 4.18454

    7aaa97 1.6737 2.64392 2.10776 1.88584 1.73189 2.31459 2.3403 1.7287

  • 49

    7Ac26E 1.97428 2.25272 2.21047 2.46443 2.10831 2.18318 1.88301 2.05127

    7bb4aa 1.70021 1.55228 1.83429 2.10791 2.1738 1.90035 2.36803 1.82228

    7d2c6a 2.49998 1.43285 2.09022 3.97653 1.66839 3.36722 2.64493 5.29335

    7E2A17 1.80226 1.40236 2.25854 1.72669 1.82877 2.05181 2.58103 1.6417

    80758E 1.37963 1.83979 2.11977 2.44002 1.4002 3.91241 2.23471 2.05295

    81086B 1.35123 1.86599 1.9636 1.3883 1.8856 2.47651 3.42017 4.17703

    83F325 2.53268 1.94683 1.98038 2.38483 2.54848 2.43137 2.04902 2.14318

    F7B78 1.30737 1.10167 7.60972 1.61577 1.74766 5.6304 2.1284 1.52618

    Idcodes Screen25 Screen26 Screen27 Screen28 Screen29 Screen30 Screen31 Screen32

    16183E 2.43033 3.47283 2.65075 2.22559 2.63456 5.237 1.99701 3.15063

    1651CA 2.32026 3.78487 2.0684 1.58359 1.52267 2.58835 4.98108 10.1459

    1a39de 1.91543 1.88353 1.85983 1.42407 2.09942 2.35206 2.11405 3.69314

    1C5882 3.15717 1.93345 4.14721 2.33351 2.18771 1.73369 3.2117 7.73238

    1E1E75 2.53071 2.13057 3.07107 1.94527 7.33142 4.85737 1.8782 3.83885

    2018DB 2.13093 1.61727 3.42896 1.35145 1.99341 2.58746 1.82146 6.7713

    292EDE 2.2922 1.97848 3.06243 3.59441 2.08421 3.33274 15.3649 2.29159

    2d0dbc 3.62375 1.71622 2.25253 1.30594 1.39637 2.06848 1.5343 7.13371

    2DA18E 2.2179 2.4537 2.89982 6.35187 5.11406 4.46045 2.48816 6.49507

    2EBCCC 3.91146 1.5055 2.28375 1.53951 2.80756 2.1577 1.5895 3.68813

    32AF92 2.73174 1.72939 6.28798 2.0139 2.25182 3.02586 5.15644 17.599

    339A4C-B 3.03189 2.53081 1.793 1.81839 3.18309 3.59493 2.40428 4.66965

    33E973 2.23567 2.8677 2.18965 1.21635 1.74805 2.59007 3.88842 5.70356

    36DADB 3.36111 3.1053 2.11532 1.45158 2.3694 2.48931 2.45368 8.48821

    36DEAB 1.73423 2.8681 5.10172 4.21843 2.97423 8.46973 2.98284 4.64332

    3824D3 1.61159 2.81014 3.04798 1.85391 1.82957 2.07009 3.31205 3.29894

    3B45E9 3.20505 2.64312 2.78696 4.29546 3.39308 10.3716 1.7693 15.0168

    3F0272 2.92818 1.55003 7.90447 1.83618 16.7977 3.83746 2.3972 3.70172

    434EFD 2.28579 2.82019 2.296 1.78821 2.44595 2.60468 2.06843 5.29567

    48B200 1.96148 2.61772 2.93389 3.6533 2.25053 3.25646 1.28376 2.83762

    49D9A4 2.98867 3.15869 2.71097 1.40169 1.96901 2.87144 3.72976 3.19195

    4A64E0 2.72309 2.63574 2.85906 1.76747 2.6465 5.06809 3.10183 3.38833

    4C51ED 1.95561 2.14762 2.49372 1.71666 2.38946 3.25417 2.54808 3.49694

    50FA05 2.17098 2.00951 2.17511 1.99664 2.64329 7.98026 10.249 10.7832

    5abo3f 1.88607 2.38801 2.51828 1.47031 3.70491 2.64425 3.82078 4.23334

    5BFC20 2.53134 1.60135 2.38572 1.25122 2.8895 1.83953 1.67142 3.39565

    5DD4332 2.61971 1.68849 2.11791 1.55238 3.20914 2.28613 1.61217 7.27517

    5E7C0B 2.9395 1.99931 2.42377 1.37959 4.1678 2.47901 3.35795 12.9429

    5EE7CF 3.34834 2.20527 6.02058 1.46496 8.79564 8.94979 3.32618 4.17409

    5FF4BE 2.07498 2.98875 2.32693 1.54896 1.80673 3.28533 7.02726 4.21404

    619A45 2.96243 2.72253 7.27473 2.0608 2.41681 2.83697 2.63717 4.0857

    64C7C1 2.15903 3.20719 2.50103 1.58857 2.30748 2.32556 2.03535 4.17188

    6a7527 3.31612 1.97199 3.77001 1.73623 3.44429 3.18337 2.56825 7.15077

    6C8489 3.10162 4.39372 2.40383 1.39579 3.0763 2.09188 2.51406 1.90397

    6DA768 2.19879 3.09531 6.6199 1.40745 2.2858 3.63016 29.2399 8.71958

  • 50

    77E07B 2.90813 1.78193 3.64299 1.58682 2.21265 4.50698 2.92486 6.30974

    7aaa97 4.83862 2.0949 5.46198 1.42515 2.42081 2.83939 4.35113 4.89013

    7Ac26E 2.95319 1.59595 3.84447 3.26739 3.30181 3.24177 4.31409 4.36109

    7bb4aa 2.15441 2.05236 6.26292 3.59122 2.65644 2.59775 1.98907 4.07244

    7d2c6a 2.06745 2.22128 2.72134 1.97737 2.3336 2.83744 6.93183 4.49835

    7E2A17 2.29721 2.47754 2.48546 1.97162 4.08988 2.42361 3.01221 3.12229

    80758E 2.01077 1.56878 2.0385 1.48717 5.45325 2.44336 1.25096 12.3877

    81086B 2.45255 1.70684 6.80447 4.79702 3.81279 2.82723 1.88574 4.28423

    83F325 1.97155 2.81728 4.39738 3.2056 5.77181 7.76247 2.53814 4.85499

    F7B78 1.58832 4.21498 2.97253 1.45321 3.6488 4.15927 4.23925 2.74418

    Idcodes Screen33 Screen34 Screen35 Screen36 Screen37 Screen38 Screen39 Screen40

    16183E 1.74367 2.21284 1.77751 3.58998 2.16799 2.70207 2.2419 4.25827

    1651CA 2.29965 4.88363 1.71206 2.98602 1.70006 4.21289 2.8225 4.38687

    1a39de 2.13033 2.11275 2.91448 3.04525 2.70719 13.5814 2.73811 8.8871

    1C5882 1.7282 2.48685 1.98853 2.34343 2.7231 4.51536 1.49896 2.72845

    1E1E75 2.08657 4.48433 3.01227 4.29085 2.30454 5.54533 9.45516 5.23558

    2018DB 1.8962 4.53238 2.44204 2.05053 2.5286 7.762 3.03097 2.96104

    292EDE 2.40207 1.91366 2.09954 6.0403 7.19207 3.93847 2.87818 2.18252

    2d0dbc 1.93119 6.61838 1.86142 1.97763 4.44541 5.08389 1.68397 7.19135

    2DA18E 2.37116 2.22525 4.50713 8.39761 2.98943 8.36784 19.8635 2.67639

    2EBCCC 2.13445 3.35738 1.70858 1.88696 2.07642 3.55694 2.91675 5.63733

    32AF92 2.11708 4.39026 1.89527 5.71058 2.18151 5.05419 21.7052 3.29068

    339A4C-B 2.43124 4.30574 2.18039 4.77568 1.86006 4.74903 2.82566 1.89183

    33E973 1.76348 8.14119 1.71875 4.54311 3.93957 3.92804 3.46983 4.23991

    36DADB 2.05045 3.40075 3.12895 2.25479 2.50059 6.27366 2.60297 6.07918

    36DEAB 2.67525 10.3805 2.66371 3.04361 4.40404 3.21212 4.37008 2.74434

    3824D3 1.93484 4.38112 1.54507 4.23552 1.67593 4.82732 1.49337 3.21961

    3B45E9 2.54081 1.72074 2.39284 2.64306 9.04294 3.46726 3.19747 4.46291

    3F0272 1.96954 2.3785 8.50815 6.75849 4.22244 7.77881 5.39906 8.79142

    434EFD 2.01155 16.308 2.33136 3.71437 2.64373 7.58794 2.03015 9.40035

    48B200 2.24558 3.30716 2.18884 1.90293 5.62831 11.8299 6.13613 2.3408

    49D9A4 1.83402 1.83596 2.23612 3.78027 3.75236 10.0908 2.92831 2.31866

    4A64E0 2.08419 3.22828 2.38235 3.03443 4.32509 3.90697 2.66878 2.53504

    4C51ED 2.19501 8.0195 2.23891 2.59906 2.311 3.33733 3.10971 2.19142

    50FA05 3.043 5.57929 2.65505 8.37955 11.2559 3.17997 2.59198 4.27414

    5abo3f 1.58737 2.05353 4.02782 2.9979 1.62139 2.64997 7.74414 3.34

    5BFC20 1.99497 1.37371 1.65911 2.55056 1.52052 3.23281 1.57059 2.7531

    5DD4332 1.41867 2.71913 1.83881 2.11985 1.72767 4.3019 2.1296 2.15951

    5E7C0B 2.16277 3.46704 1.62078 3.45717 1.34912 5.39177 2.95322 2.77153

    5EE7CF 2.04315 16.53 2.16956 5.76777 10.1989 6.58264 12.1795 10.6078

    5FF4BE 2.05188 10.0026 1.77811 2.58253 7.1708 4.52066 5.19335 2.12316

    619A45 2.36427 5.09438 2.61028 3.06799 2.79395 4.6683 3.17432 2.52434

    64C7C1 2.14584 3.14271 3.91235 3.69856 4.7583 4.45464 2.34824 7.13534

    6a7527 1.98528 3.74699 2.11727 3.69339 4.17963 4.47193 3.12205 1.65805

  • 51

    6C8489 1.94459 4.99721 2.02884 4.73836 1.85896 4.87123 2.42526 3.10774

    6DA768 4.97514 5.50008 1.90345 3.17964 2.01592 19.3105 2.13443 7.02123

    77E07B 1.95936 9.87203 2.01741 2.79037 11.625 5.62074 2.59209 15.164

    7aaa97 3.21592 6.68413 1.93199 1.90658 5.61253 11.5314 5.54512 6.804

    7Ac26E 2.30028 3.55325 1.83472 2.84468 4.3569 6.67791 6.37358 2.43527

    7bb4aa 1.76977 1.47985 1.77599 2.97975 1.88768 3.50232 2.08344 3.77076

    7d2c6a 1.89404 3.03651 1.79657 3.09284 3.26674 3.63707 2.30115 4.60125

    7E2A17 1.97824 1.6865 1.89209 2.70666 1.76483 3.9651 2.33076 3.91299

    80758E 2.34794 4.95802 2.11808 2.94232 1.73812 3.84665 2.62831 2.54301

    81086B 2.82015 16.9044 2.25185 3.33876 2.14278 4.55452 2.27653 20.9669

    83F325 2.42864 2.79896 1.9107 2.04953 2.36691 3.63726 5.18929 2.76753

    F7B78 1.73947 3.16022 1.91161 2.42555 1.84369 3.50028 1.95992 1.88043

    Idcodes Screen41 Screen42 Screen43 Screen44 Screen45 Screen46 Screen47 Screen48

    16183E 2.36611 3.46094 2.01676 3.39721 3.47721 2.51954 2.32538 2.77095

    1651CA 2.15697 3.04531 1.97885 4.05559 3.42021 3.00816 2.32644 3.93158

    1a39de 1.67546 2.57238 1.4664 1.61206 5.01063 4.02467 1.66853 1.35836

    1C5882 1.9734 2.01398 3.97773 1.38971 3.06393 2.77991 2.09582 2.55609

    1E1E75 2.58743 4.25455 1.81955 2.87983 4.24843 14.3444 2.33102 3.20072

    2018DB 1.59328 2.972 1.88996 1.96806 2.0934 3.48361 2.06777 2.63008

    292EDE 1.82645 3.10944 4.57572 2.80352 1.70726 7.27864 2.46164 6.33816

    2d0dbc 8.8303 3.43303 2.73113 1.83335 2.45093 4.37719 2.24502 1.74773

    2DA18E 4.86618 7.30348 4.77811 3.21391 2.69954 45.5709 2.63162 5.32152

    2EBCCC 2.05284 3.53933 3.03135 1.96197 5.50563 3.2919 1.96191 2.04239

    32AF92 3.03257 2.72314 2.18729 2.16499 5.58939 3.43164 2.80177 3.59495

    339A4C-B 1.6659 4.32604 1.75458 5.157 1.97219 10.7555 2.49122 3.36945

    33E973 2.83898 2.45623 1.52445 5.25713 2.67187 2.42627 1.71579 2.10011

    36DADB 3.70752 2.57354 2.26318 1.80025 4.94449 6.23667 2.09238 1.85074

    36DEAB 5.82023 3.06878 2.90659 3.28716 3.30747 3.50972 3.93528 2.35504

    3824D3 2.85633 2.61064 2.06579 1.74051 3.53591 4.04488 1.98841 1.92875

    3B45E9 3.34565 2.6118 2.97778 3.84808 2.20383 3.91717 2.86051 2.16849

    3F0272 1.873 5.45981 2.32782 5.72632 4.75647 12.3807 4.70488 2.70899

    434EFD 2.93659 3.81362 3.30746 3.64558 4.03543 7.38871 2.15021 2.60806

    48B200 2.088 3.05581 1.59478 5.0882 1.91443 4.33309 1.8914 2.27357

    49D9A4 2.63204 2.8575 3.08511 5.28384 1.68347 2.93965 2.62355 2.92397

    4A64E0 2.2509 1.92149 5.0495 2.19155 7.79387 4.36444 2.03635 2.68464

    4C51ED 2.36937 2.88854 1.67846 2.18986 2.52021 1.88269 4.48074 2.905

    50FA05 4.98043 2.79252 2.57054 14.2338 2.68346 2.43569 1.89122 2.29755

    5abo3f 2.87986 2.20045 1.63356 3.96277 1.62645 2.22922 2.35897 2.31915

    5BFC20 5.53966 3.30409 4.45983 1.26688 3.69823 7.83869 2.02206 1.94646

    5DD4332 5.23203 2.42605 5.8447 1.51682 1.63782 2.0848 2.51623 5.5968

    5E7C0B 4.80169 2.67012 4.62063 4.26478 1.6124 2.81059 1.48043 2.15417

    5EE7CF 9.30213 3.57085 5.75974 1.95318 4.82722 2.78849 2.11812 2.45396

    5FF4BE 2.03499 1.6552 1.96151 5.24961 1.83965 1.89606 1.97175 2.33793

    619A45 2.89243 3.3709 5.10537 5.15069 4.82971 4.05682 2.97184 2.41795

  • 52

    64C7C1 2.42698 2.21316 6.33963 1.76779 2.45371 8.90034 2.47207 1.82641

    6a7527 1.91801 4.50278 6.99436 1.54988 1.61481 2.3005 2.73694 3.04507

    6C8489 2.42115 2.04207 3.15409 1.79609 2.49193 2.76754 3.06402 3.62025

    6DA768 3.24877 2.86318 2.07553 8.66739 2.42149 2.86617 1.67665 1.81959

    77E07B 3.55556 3.43707 9.68572 5.45835 5.22992 3.49634 2.47768 9.73283

    7aaa97 2.74738 2.49378 5.85197 15.2587 2.53472 1.57773 4.10486 1.97262

    7Ac26E 1.85945 2.75996 4.40026 1.77633 3.63609 4.30053 2.49643 3.21333

    7bb4aa 2.32845 3.09578 4.44304 2.0277 2.40486 3.18921 3.73522 1.58707

    7d2c6a 4.84112 3.28207 4.73844 2.82272 2.51416 2.38023 2.98462 2.18037

    7E2A17 2.90768 2.11397 2.01539 2.36378 1.8441 2.44348 4.54802 2.15167

    80758E 3.76299 2.72322 1.49958 1.60744 1.88573 1.97106 1.99412 1.51527

    81086B 2.97953 2.5141 2.43407 3.78823 3.43058 8.80065 3.8251 2.99511

    83F325 4.62939 3.88046 6.26459 2.06924 2.57416 5.46918 1.77483 2.30876

    F7B78 4.11263 2.64278 2.11546 3.17934 1.83983 2.57489 2.10899 2.00743

    Idcodes Screen49 Screen50 Screen51 Screen52 Screen53 Screen54 Screen55 Screen56

    16183E 3.52153 11.6498 7.87222 2.2663 3.74868 4.23077 2.36054 5.32108

    1651CA 4.83431 3.49225 2.36192 3.33844 3.70343 5.52476 4.25169 3.22554

    1a39de 3.53457 17.9747 1.96321 1.5251 1.68952 3.09707 2.48355 2.40448

    1C5882 3.97184 2.72949 2.69636 15.0372 2.38931 7.89593 5.50784 3.16591

    1E1E75 9.66532 8.21154 2.88931 3.28777 7.54206 2.09884 12.9824 3.31041

    2018DB 3.99177 13.7547 2.82993 3.3335 2.92542 5.53116 5.21741 3.60548

    292EDE 4.9101 4.63241 6.48258 6.93123 6.75367 2.21162 7.12126 2.8596

    2d0dbc 10.215 16.9235 2.57012 2.90628 2.5548 2.3849 5.88908 3.63947

    2DA18E 7.16146 3.12477 2.73738 7.19877 6.56698 5.73288 2.87483 6.43761

    2EBCCC 2.55386 18.1906 1.59069 1.6652 17.8607 1.65608 2.32412 2.08423

    32AF92 4.66791 34.3518 3.1054 2.35548 13.5483 2.48668 10.1002 4.05668

    339A4C-B 5.24721 4.71062 8.10388 4.79476 4.08663 2.17989 5.06477 2.42948

    33E973 6.26479 35.8477 1.72409 3.14863 3.98303 5.77654 11.3187 5.09481

    36DADB 4.59289 6.91494 3.62918 2.61533 13.9721 9.6824 16.1472 3.4051

    36DEAB 3.07591 21.2059 2.234 2.08513 8.29063 5.26504 5.95656 3.10785

    3824D3 9.87693 3.03693 2.91703 9.86957 3.48773 3.23 6.88843 4.63854

    3B45E9 2.65517 12.3631 6.54734 8.34207 3.85811 4.8626 8.66641 4.49463

    3F0272 3.47908 3.9618 22.0886 2.88591 18.2378 4.39697 12.038 3.34928

    434EFD 11.0539 2.72377 2.1082 4.91113 6.37744 1.39752 3.30913 3.24016

    48B200 3.02761 14.51 3.01623 4.02072 6.35296 16.1008 2.21459 2.94123

    49D9A4 7.58808 3.25407 2.26837 2.88662 2.89285 2.73061 2.05703 6.04533

    4A64E0 3.51675 2.93264 2.37034 4.97699 2.76462 2.3251 7.03559 2.35541

    4C51ED 5.29262 4.53497 4.1448 2.9417 6.67418 9.30071 5.86405 5.82853

    50FA05 3.67751 9.80632 1.94766 3.98301 5.74718 4.85403 2.96276 2.23223

    5abo3f 2.23082 3.73883 3.42921 2.38408 2.81978 8.23185 4.57177 6.18441

    5BFC20 2.78817 3.20655 1.7642 3.69995 4.06545 5.41072 3.02451 1.90866

    5DD4332 2.21274 3.7158 2.1149 2.30215 6.6338 2.87941 4.68406 2.41228

    5E7C0B 2.77033 4.32119 4.15299 1.41102 14.8107 6.60805 6.57395 3.20477

    5EE7CF 12.1585 8.13292 8.64861 4.73755 7.30231 8.6116 21.4654 11.6198

  • 53

    5FF4BE 2.79777 2.89404 2.85218 1.89828 8.85477 8.17214 1.97934 5.65555

    619A45 15.3033 10.3066 2.37237 5.01921 2.05497 1.80824 4.35524 4.10011

    64C7C1 3.18806 3.68667 4.45292 12.0684 3.39763 5.39967 3.40578 1.86147

    6a7527 7.79521 2.61105 2.3857 3.17177 12.4668 7.34885 2.86241 2.95448

    6C8489 11.9851 6.17133 1.50504 5.91584 1.80294 4.63593 7.62019 6.48433

    6DA768 3.1363 3.83626 6.92465 2.94056 7.32862 8.45573 5.86786 2.42166

    77E07B 4.25971 4.74474 13.7879 3.7834 3.7059 1.9589 3.50026 4.97022

    7aaa97 2.25859 2.27882 5.51128 10.7779 2.66179 7.76248 4.03402 3.18366

    7Ac26E 2.89886 3.05524 3.33067 4.44969 14.5038 2.67749 4.22007 3.08064

    7bb4aa 3.58131 12.3301 2.73582 3.04453 3.38069 5.61691 3.59718 5.06081

    7d2c6a 3.11246 15.4921 2.36104 4.04487 10.6819 2.21825 2.01425 2.42472

    7E2A17 3.01638 5.52712 2.25062 4.08111 3.09751 10.7977 2.47778 4.89449

    80758E 3.284 4.51836 2.06784 5.14222 1.61039 1.87218 2.80422 2.7945

    81086B 4.27503 7.39563 5.01978 4.58404 2.02733 5.1104 2.37609 5.70083

    83F325 7.32755 5.22907 2.54342 2.79224 8.75056 1.79933 5.26429 7.71679

    F7B78 6.14938 17.0001 4.26433 3.21281 2.15462 1.87694 12.1095 4.11282

    Idcodes Screen57 Screen58 Screen59 Screen60 Screen61 Screen62 Screen63 Screen64

    16183E 2.36111 2.45698 3.97333 2.73131 1.96927 4.1282 3.94195 10.2994

    1651CA 1.80328 1.89361 2.36144 5.90556 2.33941 4.59265 1.88175 11.7472

    1a39de 5.00432 2.08838 2.93059 5.94467 2.24446 8.02942 4.61497 9.40565

    1C5882 1.16341 2.46012 6.60836 3.43853 5.5906 7.47335 4.38664 2.47772

    1E1E75 2.70902 3.1508 6.17696 5.50247 3.36164 3.82568 10.2615 22.0288

    2018DB 2.75531 3.37573 4.45572 2.70054 1.85813 3.34063 5.93684 18.3041

    292EDE 4.80623 2.42827 2.62198 3.64249 2.75445 3.73264 4.61063 3.75617

    2d0dbc 3.25513 2.51263 2.13378 3.7074 1.78161 11.4583 6.95058 9.98711

    2DA18E 15.1665 3.49189 6.00961 4.36109 3.09607 14.2521 7.48304 11.3876

    2EBCCC 2.51605 2.18055 3.20885 3.03889 2.03068 5.34462 4.92521 4.69921

    32AF92 2.12397 2.26909 26.2414 11.1939 2.36991 2.17618 18.3023 30.6514

    339A4C-B 2.15909 3.15075 5.90568 2.20433 1.79539 2.5613 4.49977 16.1858

    33E973 4.33376 3.09956 7.95195 8.54656 5.6683 5.99219 1.89636 2.63683

    36DADB 3.31512 2.9151 5.73173 4.45744 9.02987 4.48047 4.33607 3.4073

    36DEAB 3.32043 2.62185 10.6423 3.46894 2.24472 3.93298 4.10701 24.871

    3824D3 1.43016 1.86422 3.80439 2.87301 1.87895 2.04